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Introduction: Pyoderma gangrenosum (PG) poses a significant dermatological challenge due to its rapidly evolving painful necrotic ulcerations. Understanding its multifaceted pathogenesis and diverse clinical presentation is crucial for effective management., Objectives: We aimed to analyze demographic characteristics, clinical manifestations, lesion distributions, systemic disease associations, therapeutic interventions, and patient outcomes in PG cases., Methods: Medical records from 2017 to 2023 of PG patients at IRCCS Sant'Orsola Malpighi Hospital, Bologna, Italy, were retrospectively analyzed. Inclusion criteria encompassed persistent ulcers with clinical and histological evidence of PG, excluding cases with alternative diagnoses or inadequate follow-up. Clinical evaluations, including pain assessment and lesion measurements, were conducted at diagnosis and follow-up visits., Results: A total of 44 patients were evaluated. Pain was a universal symptom, and tissue pathergy was documented in 28.6% of patients. Ulcerative PG was the most common subtype (88.1%). Associations with inflammatory bowel diseases (25%), rheumatoid arthritis (9.1%), and hematological diseases (17.2%) were noted. Lower limbs were frequently affected (63.6%). Treatment approaches included wound management, topical and systemic corticosteroids, and immunosuppressive therapy, with varying response rates., Conclusion: Advanced dressing and steroid therapy were pivotal in mild PG cases, while moderate-to-severe cases often associated with systemic diseases showed incomplete healing despite treatment, especially in patients with inflammatory bowel diseases and hematological disorders. This study contributes to the understanding of PG's complexities, suggesting the use of biological therapy as first line in moderate-to-severe PG.

Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully automated image processing pipeline leveraging deep learning and a large wound segmentation dataset to perform wound detection and following prediction of the Photographic Wound Assessment Tool (PWAT), automatizing the clinical judgement of the adequate wound healing. Starting from images acquired by smartphone cameras, a series of textural and morphological features are extracted from the wound areas, aiming to mimic the typical clinical considerations for wound assessment. The resulting extracted features can be easily interpreted by the clinician and allow a quantitative estimation of the PWAT scores. The features extracted from the region-of-interests detected by our pre-trained neural network model correctly predict the PWAT scale values with a Spearman's correlation coefficient of 0.85 on a set of unseen images. The obtained results agree with the current state-of-the-art and provide a benchmark for future artificial intelligence applications in this research field., (© 2024. The Author(s).)

Appropriate wound management shortens the healing times and reduces the management costs, benefiting the patient in physical terms and potentially reducing the healthcare system's economic burden. Among the instrumental measurement methods, the image analysis of a wound area is becoming one of the cornerstones of chronic ulcer management. Our study aim is to develop a solid AI method based on a convolutional neural network to segment the wounds efficiently to make the work of the physician more efficient, and subsequently, to lay the foundations for the further development of more in-depth analyses of ulcer characteristics. In this work, we introduce a fully automated model for identifying and segmenting wound areas which can completely automatize the clinical wound severity assessment starting from images acquired from smartphones. This method is based on an active semi-supervised learning training of a convolutional neural network model. In our work, we tested the robustness of our method against a wide range of natural images acquired in different light conditions and image expositions. We collected the images using an ad hoc developed app and saved them in a database which we then used for AI training. We then tested different CNN architectures to develop a balanced model, which we finally validated with a public dataset. We used a dataset of images acquired during clinical practice and built an annotated wound image dataset consisting of 1564 ulcer images from 474 patients. Only a small part of this large amount of data was manually annotated by experts (ground truth). A multi-step, active, semi-supervised training procedure was applied to improve the segmentation performances of the model. The developed training strategy mimics a continuous learning approach and provides a viable alternative for further medical applications. We tested the efficiency of our model against other public datasets, proving its robustness. The efficiency of the transfer learning showed that after less than 50 epochs, the model achieved a stable DSC that was greater than 0.95. The proposed active semi-supervised learning strategy could allow us to obtain an efficient segmentation method, thereby facilitating the work of the clinician by reducing their working times to achieve the measurements. Finally, the robustness of our pipeline confirms its possible usage in clinical practice as a reliable decision support system for clinicians.

Background: Real-world data for guselkumab, the first interleukin-23 inhibitor approved to treat moderate-to-severe psoriasis, are scarce. This study represents the first 60-week, real-life, multicenter, retrospective experience to investigate the effectiveness, safety, tolerability, and drug retention of guselkumab in psoriatic patients., Research Design and Methods: Clinical information was collected at baseline and at weeks 12, 24, 36, 48, and 60., Results: The mean baseline Psoriasis Activity Severity Index (PASI) reduced from 14.2 to 3.1 at week 12 and decreased to around 0 at weeks 36, 48, and 60. PASI 75, PASI 90, and PASI 100 were 100%, 96.8%, and 83.9% at week 60, respectively. Multiple logistic regression analysis showed that neither body mass index >30, smoking, ≥3 comorbidities, difficult-to-treat areas, nor a failure to ≥2 prior biologic treatments significantly influenced PASI reduction (p > 0.05)., Conclusions: Our findings confirm guselkumab as an appropriate therapeutic option in routine clinical practice, especially when dealing with complex patients with comorbidities or previous failure to biologic treatments.

Objective: To evaluate the clinical efficacy of a hypericum and neem oil dressing, Primary Wound Dressing [ONE] (1PWD) (Kerecis AG, Switzerland), in a patient population with dehiscence of surgical wounds with critical colonisation/infection. Efficacy was defined as resolution of inflammatory/infective symptoms., Method: A randomised, controlled, single-blinded, parallel-arms phase III study was conducted comparing the experimental medication to silver-based dressings. All patients were evaluated at enrolment, on days 7, 14, 21 and 28. Improvement of inflammatory/infective symptoms was measured by detecting seven items of the Bates-Jensen Wound Assessment Tool (BWAT). Pain was assessed using the Numeric Rating Scale (NRS)., Results: The study enrolled 99 patients. Follow-up was completed in 49 patients in the experimental group and 48 patients in the control group. Overall BWAT evaluation demonstrated similar outcomes between the groups: t=0.23, p-value=0.81, 95% confidence interval (CI): -13.3-10.8. Furthermore, when evaluating the seven items of the BWAT relating to inflammatory signs, there was not a significant difference between the groups: t=0.38, p=0.35, 95% CI: -2.8-2.7. However, when an analysis using the NRS pain scale was performed, a statistically significant pain reduction was demonstrated in favour of the experimental group: t=7.8, p<0.0001, 95% CI: 2.918-4.8819., Conclusion: This randomised controlled trial confirmed the efficacy of 1PWD, an investigational product, in the management of surgical dehiscence with critical colonisation or infection, with the added benefit of significant pain reduction when compared with a silver-based dressing.